Overview

Dataset statistics

Number of variables36
Number of observations459
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory129.2 KiB
Average record size in memory288.3 B

Variable types

Categorical30
Numeric6

Alerts

nazwa has a high cardinality: 439 distinct values High cardinality
miasto has a high cardinality: 56 distinct values High cardinality
firma has a high cardinality: 128 distinct values High cardinality
liczba_ocen has a high cardinality: 193 distinct values High cardinality
miejsce_w_polsce is highly correlated with srednia_ocena and 1 other fieldsHigh correlation
bezpieczeństwo_dezynfekowane_pokoje is highly correlated with bezpieczeństwo_dostępny_żel_antybakteryjny and 1 other fieldsHigh correlation
bezpieczeństwo_dostępny_żel_antybakteryjny is highly correlated with bezpieczeństwo_dezynfekowane_pokoje and 1 other fieldsHigh correlation
bezpieczeństwo_przycisk_bezpieczeństwa is highly correlated with bezpieczeństwo_system_awaryjnego_otwierania_drzwiHigh correlation
bezpieczeństwo_pełen_monitoring is highly correlated with bezpieczeństwo_pełen_kontakt_z_mistrzem_gryHigh correlation
bezpieczeństwo_pełen_kontakt_z_mistrzem_gry is highly correlated with bezpieczeństwo_pełen_monitoringHigh correlation
bezpieczeństwo_system_awaryjnego_otwierania_drzwi is highly correlated with bezpieczeństwo_przycisk_bezpieczeństwaHigh correlation
bezpieczeństwo_dostępne_rekawiczki is highly correlated with bezpieczeństwo_dezynfekowane_pokoje and 1 other fieldsHigh correlation
srednia_ocena is highly correlated with miejsce_w_polsce and 2 other fieldsHigh correlation
ocena_obsluga is highly correlated with srednia_ocena and 1 other fieldsHigh correlation
ocena_klimat is highly correlated with miejsce_w_polsce and 2 other fieldsHigh correlation
miejsce_w_polsce is highly correlated with srednia_ocena and 1 other fieldsHigh correlation
bezpieczeństwo_dezynfekowane_pokoje is highly correlated with bezpieczeństwo_dostępny_żel_antybakteryjny and 1 other fieldsHigh correlation
bezpieczeństwo_dostępny_żel_antybakteryjny is highly correlated with bezpieczeństwo_dezynfekowane_pokoje and 1 other fieldsHigh correlation
bezpieczeństwo_przycisk_bezpieczeństwa is highly correlated with bezpieczeństwo_system_awaryjnego_otwierania_drzwiHigh correlation
bezpieczeństwo_pełen_monitoring is highly correlated with bezpieczeństwo_pełen_kontakt_z_mistrzem_gryHigh correlation
bezpieczeństwo_pełen_kontakt_z_mistrzem_gry is highly correlated with bezpieczeństwo_pełen_monitoringHigh correlation
bezpieczeństwo_system_awaryjnego_otwierania_drzwi is highly correlated with bezpieczeństwo_przycisk_bezpieczeństwaHigh correlation
bezpieczeństwo_dostępne_rekawiczki is highly correlated with bezpieczeństwo_dezynfekowane_pokoje and 1 other fieldsHigh correlation
srednia_ocena is highly correlated with miejsce_w_polsce and 2 other fieldsHigh correlation
ocena_obsluga is highly correlated with srednia_ocena and 1 other fieldsHigh correlation
ocena_klimat is highly correlated with miejsce_w_polsce and 2 other fieldsHigh correlation
miejsce_w_polsce is highly correlated with srednia_ocena and 1 other fieldsHigh correlation
bezpieczeństwo_dezynfekowane_pokoje is highly correlated with bezpieczeństwo_dostępny_żel_antybakteryjny and 1 other fieldsHigh correlation
bezpieczeństwo_dostępny_żel_antybakteryjny is highly correlated with bezpieczeństwo_dezynfekowane_pokoje and 1 other fieldsHigh correlation
bezpieczeństwo_przycisk_bezpieczeństwa is highly correlated with bezpieczeństwo_system_awaryjnego_otwierania_drzwiHigh correlation
bezpieczeństwo_pełen_monitoring is highly correlated with bezpieczeństwo_pełen_kontakt_z_mistrzem_gryHigh correlation
bezpieczeństwo_pełen_kontakt_z_mistrzem_gry is highly correlated with bezpieczeństwo_pełen_monitoringHigh correlation
bezpieczeństwo_system_awaryjnego_otwierania_drzwi is highly correlated with bezpieczeństwo_przycisk_bezpieczeństwaHigh correlation
bezpieczeństwo_dostępne_rekawiczki is highly correlated with bezpieczeństwo_dezynfekowane_pokoje and 1 other fieldsHigh correlation
srednia_ocena is highly correlated with miejsce_w_polsce and 1 other fieldsHigh correlation
ocena_klimat is highly correlated with miejsce_w_polsce and 1 other fieldsHigh correlation
bezpieczeństwo_system_awaryjnego_otwierania_drzwi is highly correlated with bezpieczeństwo_przycisk_bezpieczeństwaHigh correlation
bezpieczeństwo_pełen_monitoring is highly correlated with bezpieczeństwo_pełen_kontakt_z_mistrzem_gryHigh correlation
bezpieczeństwo_dostępne_rekawiczki is highly correlated with bezpieczeństwo_dostępny_żel_antybakteryjny and 1 other fieldsHigh correlation
bezpieczeństwo_pełen_kontakt_z_mistrzem_gry is highly correlated with bezpieczeństwo_pełen_monitoring and 1 other fieldsHigh correlation
miasto is highly correlated with bezpieczeństwo_pełen_kontakt_z_mistrzem_gry and 2 other fieldsHigh correlation
bezpieczeństwo_przycisk_bezpieczeństwa is highly correlated with bezpieczeństwo_system_awaryjnego_otwierania_drzwiHigh correlation
bezpieczeństwo_dostępny_żel_antybakteryjny is highly correlated with bezpieczeństwo_dostępne_rekawiczki and 2 other fieldsHigh correlation
bezpieczeństwo_dezynfekowane_pokoje is highly correlated with bezpieczeństwo_dostępne_rekawiczki and 2 other fieldsHigh correlation
miasto is highly correlated with czas_gry and 18 other fieldsHigh correlation
czas_gry is highly correlated with miastoHigh correlation
poziom_trudnosci is highly correlated with miastoHigh correlation
miejsce_w_polsce is highly correlated with miasto and 2 other fieldsHigh correlation
bezpieczeństwo_tylko_niskie_napięcie_w_zasięgu_gracza is highly correlated with miasto and 3 other fieldsHigh correlation
bezpieczeństwo_dezynfekowane_pokoje is highly correlated with miasto and 5 other fieldsHigh correlation
podstawowe_klimatyzowany is highly correlated with miastoHigh correlation
podstawowe_nie_dla_epileptyków is highly correlated with podstawowe_nieodpowiednie_dla_osób_z_klaustrofobią and 1 other fieldsHigh correlation
bezpieczeństwo_ograniczony_kontakt_z_innymi_grupami is highly correlated with bezpieczeństwo_dezynfekowane_pokoje and 2 other fieldsHigh correlation
bezpieczeństwo_oświetlenie_awaryjne is highly correlated with miasto and 2 other fieldsHigh correlation
podstawowe_przyjazny_zwierzętom is highly correlated with miasto and 2 other fieldsHigh correlation
bezpieczeństwo_dostępny_żel_antybakteryjny is highly correlated with miasto and 4 other fieldsHigh correlation
bezpieczeństwo_przycisk_bezpieczeństwa is highly correlated with miasto and 2 other fieldsHigh correlation
podstawowe_nieodpowiednie_dla_osób_z_klaustrofobią is highly correlated with podstawowe_nie_dla_epileptyków and 1 other fieldsHigh correlation
bezpieczeństwo_drzwi_zawsze_otwarte is highly correlated with miastoHigh correlation
bezpieczeństwo_pełen_monitoring is highly correlated with miasto and 3 other fieldsHigh correlation
bezpieczeństwo_pełen_kontakt_z_mistrzem_gry is highly correlated with miasto and 2 other fieldsHigh correlation
bezpieczeństwo_system_awaryjnego_otwierania_drzwi is highly correlated with miasto and 1 other fieldsHigh correlation
języki_angielski is highly correlated with miastoHigh correlation
podstawowe_możliwość_płatności_kartą is highly correlated with miastoHigh correlation
bezpieczeństwo_dostępne_rekawiczki is highly correlated with miasto and 3 other fieldsHigh correlation
podstawowe_nie_dla_kobiet_w_ciąży is highly correlated with podstawowe_nie_dla_epileptyków and 2 other fieldsHigh correlation
podstawowe_przyjazny_dzieciom is highly correlated with podstawowe_od_16_latHigh correlation
podstawowe_od_16_lat is highly correlated with podstawowe_nie_dla_kobiet_w_ciąży and 1 other fieldsHigh correlation
srednia_ocena is highly correlated with miejsce_w_polsce and 2 other fieldsHigh correlation
ocena_obsluga is highly correlated with miasto and 2 other fieldsHigh correlation
ocena_klimat is highly correlated with miasto and 3 other fieldsHigh correlation
nazwa is uniformly distributed Uniform
miejsce_w_polsce is uniformly distributed Uniform
miejsce_w_polsce has unique values Unique

Reproduction

Analysis started2022-05-10 12:16:27.685227
Analysis finished2022-05-10 12:16:42.232992
Duration14.55 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

nazwa
Categorical

HIGH CARDINALITY
UNIFORM

Distinct439
Distinct (%)95.6%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
Saloon
 
3
Łódź Podwodna
 
3
Kraina Czarów
 
3
Chata Wiedźmy
 
3
W cieniu piramid
 
2
Other values (434)
445 

Length

Max length41
Median length28
Mean length15.23529412
Min length4

Characters and Unicode

Total characters6993
Distinct characters84
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique423 ?
Unique (%)92.2%

Sample

1st rowCicha Noc
2nd rowPowstanie Warszawskie
3rd rowLokalizacja
4th rowOpuszczony Hotel
5th rowRastamobil

Common Values

ValueCountFrequency (%)
Saloon3
 
0.7%
Łódź Podwodna3
 
0.7%
Kraina Czarów3
 
0.7%
Chata Wiedźmy3
 
0.7%
W cieniu piramid2
 
0.4%
Olimp2
 
0.4%
Skarbiec2
 
0.4%
Redrum2
 
0.4%
Testament2
 
0.4%
Eksperyment2
 
0.4%
Other values (429)435
94.8%

Length

2022-05-10T14:16:42.325359image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
28
 
2.7%
w22
 
2.2%
tajemnica12
 
1.2%
klątwa9
 
0.9%
2.09
 
0.9%
na9
 
0.9%
i8
 
0.8%
pokój8
 
0.8%
z8
 
0.8%
majów7
 
0.7%
Other values (678)903
88.3%

Most occurring characters

ValueCountFrequency (%)
a742
 
10.6%
564
 
8.1%
e461
 
6.6%
i452
 
6.5%
o384
 
5.5%
r380
 
5.4%
n348
 
5.0%
t256
 
3.7%
k210
 
3.0%
z205
 
2.9%
Other values (74)2991
42.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5444
77.8%
Uppercase Letter849
 
12.1%
Space Separator564
 
8.1%
Other Punctuation57
 
0.8%
Decimal Number48
 
0.7%
Dash Punctuation28
 
0.4%
Close Punctuation2
 
< 0.1%
Open Punctuation1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a742
13.6%
e461
 
8.5%
i452
 
8.3%
o384
 
7.1%
r380
 
7.0%
n348
 
6.4%
t256
 
4.7%
k210
 
3.9%
z205
 
3.8%
s196
 
3.6%
Other values (24)1810
33.2%
Uppercase Letter
ValueCountFrequency (%)
S95
 
11.2%
P89
 
10.5%
K67
 
7.9%
M62
 
7.3%
C53
 
6.2%
T52
 
6.1%
W45
 
5.3%
A34
 
4.0%
Z33
 
3.9%
O32
 
3.8%
Other values (16)287
33.8%
Decimal Number
ValueCountFrequency (%)
214
29.2%
013
27.1%
46
12.5%
14
 
8.3%
34
 
8.3%
53
 
6.2%
91
 
2.1%
61
 
2.1%
81
 
2.1%
71
 
2.1%
Other Punctuation
ValueCountFrequency (%)
.35
61.4%
:10
 
17.5%
'3
 
5.3%
!3
 
5.3%
"2
 
3.5%
/1
 
1.8%
@1
 
1.8%
·1
 
1.8%
?1
 
1.8%
Dash Punctuation
ValueCountFrequency (%)
-27
96.4%
1
 
3.6%
Space Separator
ValueCountFrequency (%)
564
100.0%
Close Punctuation
ValueCountFrequency (%)
)2
100.0%
Open Punctuation
ValueCountFrequency (%)
(1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6293
90.0%
Common700
 
10.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a742
 
11.8%
e461
 
7.3%
i452
 
7.2%
o384
 
6.1%
r380
 
6.0%
n348
 
5.5%
t256
 
4.1%
k210
 
3.3%
z205
 
3.3%
s196
 
3.1%
Other values (50)2659
42.3%
Common
ValueCountFrequency (%)
564
80.6%
.35
 
5.0%
-27
 
3.9%
214
 
2.0%
013
 
1.9%
:10
 
1.4%
46
 
0.9%
14
 
0.6%
34
 
0.6%
'3
 
0.4%
Other values (14)20
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII6750
96.5%
None242
 
3.5%
Punctuation1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a742
 
11.0%
564
 
8.4%
e461
 
6.8%
i452
 
6.7%
o384
 
5.7%
r380
 
5.6%
n348
 
5.2%
t256
 
3.8%
k210
 
3.1%
z205
 
3.0%
Other values (61)2748
40.7%
None
ValueCountFrequency (%)
ó64
26.4%
ł45
18.6%
ą23
 
9.5%
ż19
 
7.9%
ę19
 
7.9%
ś18
 
7.4%
Ś16
 
6.6%
ź15
 
6.2%
ć9
 
3.7%
Ł7
 
2.9%
Other values (2)7
 
2.9%
Punctuation
ValueCountFrequency (%)
1
100.0%

miasto
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct56
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
Warszawa
54 
Poznań
50 
Wrocław
49 
Kraków
37 
Bydgoszcz
 
22
Other values (51)
247 

Length

Max length19
Median length14
Mean length7.159041394
Min length4

Characters and Unicode

Total characters3286
Distinct characters51
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)1.7%

Sample

1st rowGliwice
2nd rowWarszawa
3rd rowGliwice
4th rowWrocław
5th rowWrocław

Common Values

ValueCountFrequency (%)
Warszawa54
 
11.8%
Poznań50
 
10.9%
Wrocław49
 
10.7%
Kraków37
 
8.1%
Bydgoszcz22
 
4.8%
Gdańsk21
 
4.6%
Łódź20
 
4.4%
Katowice16
 
3.5%
Bielsko-Biała12
 
2.6%
Toruń10
 
2.2%
Other values (46)168
36.6%

Length

2022-05-10T14:16:42.435974image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
warszawa54
 
11.3%
poznań50
 
10.5%
wrocław49
 
10.3%
kraków37
 
7.7%
bydgoszcz22
 
4.6%
gdańsk21
 
4.4%
łódź20
 
4.2%
katowice16
 
3.3%
bielsko-biała12
 
2.5%
sącz10
 
2.1%
Other values (50)187
39.1%

Most occurring characters

ValueCountFrequency (%)
a438
 
13.3%
o252
 
7.7%
z240
 
7.3%
w210
 
6.4%
r195
 
5.9%
s154
 
4.7%
c143
 
4.4%
i130
 
4.0%
k121
 
3.7%
W112
 
3.4%
Other values (41)1291
39.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2763
84.1%
Uppercase Letter491
 
14.9%
Space Separator19
 
0.6%
Dash Punctuation13
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a438
15.9%
o252
 
9.1%
z240
 
8.7%
w210
 
7.6%
r195
 
7.1%
s154
 
5.6%
c143
 
5.2%
i130
 
4.7%
k121
 
4.4%
n106
 
3.8%
Other values (21)774
28.0%
Uppercase Letter
ValueCountFrequency (%)
W112
22.8%
K63
12.8%
B58
11.8%
P54
11.0%
G50
10.2%
S35
 
7.1%
R20
 
4.1%
Ł20
 
4.1%
T15
 
3.1%
Z12
 
2.4%
Other values (8)52
10.6%
Space Separator
ValueCountFrequency (%)
19
100.0%
Dash Punctuation
ValueCountFrequency (%)
-13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3254
99.0%
Common32
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a438
 
13.5%
o252
 
7.7%
z240
 
7.4%
w210
 
6.5%
r195
 
6.0%
s154
 
4.7%
c143
 
4.4%
i130
 
4.0%
k121
 
3.7%
W112
 
3.4%
Other values (39)1259
38.7%
Common
ValueCountFrequency (%)
19
59.4%
-13
40.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII2966
90.3%
None320
 
9.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a438
14.8%
o252
 
8.5%
z240
 
8.1%
w210
 
7.1%
r195
 
6.6%
s154
 
5.2%
c143
 
4.8%
i130
 
4.4%
k121
 
4.1%
W112
 
3.8%
Other values (30)971
32.7%
None
ValueCountFrequency (%)
ó83
25.9%
ń82
25.6%
ł81
25.3%
ź20
 
6.2%
Ł20
 
6.2%
ą13
 
4.1%
ę6
 
1.9%
ś5
 
1.6%
ć4
 
1.2%
ż3
 
0.9%

firma
Categorical

HIGH CARDINALITY

Distinct128
Distinct (%)27.9%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
Open The Lock
 
19
LetMeOut
 
16
Wyjście Awaryjne
 
14
ExitRoom ™
 
11
Exit19.pl
 
11
Other values (123)
388 

Length

Max length42
Median length26
Mean length14.58169935
Min length6

Characters and Unicode

Total characters6693
Distinct characters73
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)4.8%

Sample

1st rowTickTack
2nd rowBlack Cat Escape Room
3rd rowTickTack
4th rowExit19.pl
5th rowEscape Bus / Escapeo

Common Values

ValueCountFrequency (%)
Open The Lock19
 
4.1%
LetMeOut16
 
3.5%
Wyjście Awaryjne14
 
3.1%
ExitRoom ™11
 
2.4%
Exit19.pl11
 
2.4%
Escapelandia10
 
2.2%
Secret Room & Brain Code10
 
2.2%
Room Escape Warszawa9
 
2.0%
Gamescape Kraków8
 
1.7%
Questrooms8
 
1.7%
Other values (118)343
74.7%

Length

2022-05-10T14:16:42.529738image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
escape117
 
10.6%
room82
 
7.4%
the41
 
3.7%
29
 
2.6%
zagadek29
 
2.6%
lock26
 
2.4%
open22
 
2.0%
kraków17
 
1.5%
letmeout16
 
1.4%
dom15
 
1.4%
Other values (173)710
64.3%

Most occurring characters

ValueCountFrequency (%)
645
 
9.6%
e593
 
8.9%
a560
 
8.4%
o486
 
7.3%
c301
 
4.5%
s278
 
4.2%
m217
 
3.2%
E210
 
3.1%
i205
 
3.1%
p197
 
2.9%
Other values (63)3001
44.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4474
66.8%
Uppercase Letter1433
 
21.4%
Space Separator645
 
9.6%
Decimal Number60
 
0.9%
Other Punctuation46
 
0.7%
Dash Punctuation24
 
0.4%
Other Symbol11
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e593
13.3%
a560
12.5%
o486
 
10.9%
c301
 
6.7%
s278
 
6.2%
m217
 
4.9%
i205
 
4.6%
p197
 
4.4%
t197
 
4.4%
r194
 
4.3%
Other values (21)1246
27.8%
Uppercase Letter
ValueCountFrequency (%)
E210
14.7%
R150
 
10.5%
T122
 
8.5%
O95
 
6.6%
L84
 
5.9%
S81
 
5.7%
C74
 
5.2%
K57
 
4.0%
A56
 
3.9%
P56
 
3.9%
Other values (17)448
31.3%
Decimal Number
ValueCountFrequency (%)
122
36.7%
911
18.3%
410
16.7%
87
 
11.7%
74
 
6.7%
24
 
6.7%
52
 
3.3%
Other Punctuation
ValueCountFrequency (%)
.24
52.2%
&13
28.3%
'5
 
10.9%
/2
 
4.3%
?2
 
4.3%
Space Separator
ValueCountFrequency (%)
645
100.0%
Dash Punctuation
ValueCountFrequency (%)
-24
100.0%
Other Symbol
ValueCountFrequency (%)
11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5907
88.3%
Common786
 
11.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e593
 
10.0%
a560
 
9.5%
o486
 
8.2%
c301
 
5.1%
s278
 
4.7%
m217
 
3.7%
E210
 
3.6%
i205
 
3.5%
p197
 
3.3%
t197
 
3.3%
Other values (48)2663
45.1%
Common
ValueCountFrequency (%)
645
82.1%
.24
 
3.1%
-24
 
3.1%
122
 
2.8%
&13
 
1.7%
11
 
1.4%
911
 
1.4%
410
 
1.3%
87
 
0.9%
'5
 
0.6%
Other values (5)14
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII6607
98.7%
None75
 
1.1%
Letterlike Symbols11
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
645
 
9.8%
e593
 
9.0%
a560
 
8.5%
o486
 
7.4%
c301
 
4.6%
s278
 
4.2%
m217
 
3.3%
E210
 
3.2%
i205
 
3.1%
p197
 
3.0%
Other values (54)2915
44.1%
None
ValueCountFrequency (%)
ó21
28.0%
ł18
24.0%
ś18
24.0%
ń6
 
8.0%
ć4
 
5.3%
Ó3
 
4.0%
ą3
 
4.0%
Ś2
 
2.7%
Letterlike Symbols
ValueCountFrequency (%)
11
100.0%

czas_gry
Real number (ℝ≥0)

HIGH CORRELATION

Distinct14
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.24400871
Minimum30
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2022-05-10T14:16:42.639089image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile60
Q160
median60
Q370
95-th percentile90
Maximum180
Range150
Interquartile range (IQR)10

Descriptive statistics

Standard deviation14.16224384
Coefficient of variation (CV)0.2137890523
Kurtosis16.1657691
Mean66.24400871
Median Absolute Deviation (MAD)0
Skewness3.281186992
Sum30406
Variance200.5691507
MonotonicityNot monotonic
2022-05-10T14:16:42.717216image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
60316
68.8%
7557
 
12.4%
9030
 
6.5%
7021
 
4.6%
1208
 
1.7%
807
 
1.5%
656
 
1.3%
504
 
0.9%
454
 
0.9%
1502
 
0.4%
Other values (4)4
 
0.9%
ValueCountFrequency (%)
301
 
0.2%
454
 
0.9%
504
 
0.9%
60316
68.8%
656
 
1.3%
7021
 
4.6%
7557
 
12.4%
807
 
1.5%
9030
 
6.5%
1001
 
0.2%
ValueCountFrequency (%)
1801
 
0.2%
1502
 
0.4%
1208
 
1.7%
1011
 
0.2%
1001
 
0.2%
9030
6.5%
807
 
1.5%
7557
12.4%
7021
 
4.6%
656
 
1.3%

kategoria
Categorical

Distinct11
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
Przygodowy
196 
Thriller
67 
Fabularny
60 
Kryminalny
51 
Fantasy
24 
Other values (6)
61 

Length

Max length13
Median length10
Mean length9.281045752
Min length5

Characters and Unicode

Total characters4260
Distinct characters29
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st rowPrzygodowy
2nd rowHistoryczny
3rd rowFabularny
4th rowThriller
5th rowPrzygodowy

Common Values

ValueCountFrequency (%)
Przygodowy196
42.7%
Thriller67
 
14.6%
Fabularny60
 
13.1%
Kryminalny51
 
11.1%
Fantasy24
 
5.2%
Horror22
 
4.8%
Historyczny15
 
3.3%
Abstrakcyjny13
 
2.8%
Dla dzieci6
 
1.3%
Akcja4
 
0.9%

Length

2022-05-10T14:16:42.810963image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
przygodowy196
42.2%
thriller67
 
14.4%
fabularny60
 
12.9%
kryminalny51
 
11.0%
fantasy24
 
5.2%
horror22
 
4.7%
historyczny15
 
3.2%
abstrakcyjny13
 
2.8%
dla6
 
1.3%
dzieci6
 
1.3%
Other values (2)5
 
1.1%

Most occurring characters

ValueCountFrequency (%)
y637
15.0%
r536
12.6%
o451
10.6%
l251
 
5.9%
a242
 
5.7%
z218
 
5.1%
n215
 
5.0%
d202
 
4.7%
P196
 
4.6%
w196
 
4.6%
Other values (19)1116
26.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3795
89.1%
Uppercase Letter459
 
10.8%
Space Separator6
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
y637
16.8%
r536
14.1%
o451
11.9%
l251
 
6.6%
a242
 
6.4%
z218
 
5.7%
n215
 
5.7%
d202
 
5.3%
w196
 
5.2%
g196
 
5.2%
Other values (11)651
17.2%
Uppercase Letter
ValueCountFrequency (%)
P196
42.7%
F85
18.5%
T67
 
14.6%
K51
 
11.1%
H37
 
8.1%
A17
 
3.7%
D6
 
1.3%
Space Separator
ValueCountFrequency (%)
6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4254
99.9%
Common6
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
y637
15.0%
r536
12.6%
o451
10.6%
l251
 
5.9%
a242
 
5.7%
z218
 
5.1%
n215
 
5.1%
d202
 
4.7%
P196
 
4.6%
w196
 
4.6%
Other values (18)1110
26.1%
Common
ValueCountFrequency (%)
6
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4260
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
y637
15.0%
r536
12.6%
o451
10.6%
l251
 
5.9%
a242
 
5.7%
z218
 
5.1%
n215
 
5.0%
d202
 
4.7%
P196
 
4.6%
w196
 
4.6%
Other values (19)1116
26.2%

poziom_trudnosci
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
śr. zaawansowani
249 
początkujący
102 
doświadczony
92 
eksperci
 
8
na pierwszy raz
 
7

Length

Max length16
Median length16
Mean length14.15250545
Min length8

Characters and Unicode

Total characters6496
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st rowśr. zaawansowani
2nd rowśr. zaawansowani
3rd rowdoświadczony
4th rowśr. zaawansowani
5th rowśr. zaawansowani

Common Values

ValueCountFrequency (%)
śr. zaawansowani249
54.2%
początkujący102
22.2%
doświadczony92
 
20.0%
eksperci8
 
1.7%
na pierwszy raz7
 
1.5%
brak informacji1
 
0.2%

Length

2022-05-10T14:16:42.920346image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T14:16:43.029714image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
śr249
34.4%
zaawansowani249
34.4%
początkujący102
14.1%
doświadczony92
 
12.7%
eksperci8
 
1.1%
na7
 
1.0%
pierwszy7
 
1.0%
raz7
 
1.0%
brak1
 
0.1%
informacji1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
a1104
17.0%
n598
 
9.2%
w597
 
9.2%
o536
 
8.3%
z457
 
7.0%
i358
 
5.5%
ś341
 
5.2%
c305
 
4.7%
r273
 
4.2%
s264
 
4.1%
Other values (14)1663
25.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5983
92.1%
Space Separator264
 
4.1%
Other Punctuation249
 
3.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a1104
18.5%
n598
10.0%
w597
10.0%
o536
9.0%
z457
 
7.6%
i358
 
6.0%
ś341
 
5.7%
c305
 
5.1%
r273
 
4.6%
s264
 
4.4%
Other values (12)1150
19.2%
Space Separator
ValueCountFrequency (%)
264
100.0%
Other Punctuation
ValueCountFrequency (%)
.249
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5983
92.1%
Common513
 
7.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a1104
18.5%
n598
10.0%
w597
10.0%
o536
9.0%
z457
 
7.6%
i358
 
6.0%
ś341
 
5.7%
c305
 
5.1%
r273
 
4.6%
s264
 
4.4%
Other values (12)1150
19.2%
Common
ValueCountFrequency (%)
264
51.5%
.249
48.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII5951
91.6%
None545
 
8.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a1104
18.6%
n598
10.0%
w597
10.0%
o536
9.0%
z457
 
7.7%
i358
 
6.0%
c305
 
5.1%
r273
 
4.6%
s264
 
4.4%
264
 
4.4%
Other values (12)1195
20.1%
None
ValueCountFrequency (%)
ś341
62.6%
ą204
37.4%

liczba_ocen
Categorical

HIGH CARDINALITY

Distinct193
Distinct (%)42.0%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
1
 
15
4
 
12
21
 
11
16
 
10
7
 
10
Other values (188)
401 

Length

Max length6
Median length3
Mean length3.128540305
Min length2

Characters and Unicode

Total characters1436
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique96 ?
Unique (%)20.9%

Sample

1st row85
2nd row258
3rd row271
4th row57
5th row98

Common Values

ValueCountFrequency (%)
1 15
 
3.3%
4 12
 
2.6%
21 11
 
2.4%
16 10
 
2.2%
7 10
 
2.2%
2 10
 
2.2%
8 9
 
2.0%
10 9
 
2.0%
6 9
 
2.0%
5 8
 
1.7%
Other values (183)356
77.6%

Length

2022-05-10T14:16:43.123497image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
118
 
3.9%
412
 
2.6%
2111
 
2.4%
1610
 
2.2%
710
 
2.2%
210
 
2.2%
89
 
1.9%
109
 
1.9%
69
 
1.9%
58
 
1.7%
Other values (182)356
77.1%

Most occurring characters

ValueCountFrequency (%)
462
32.2%
1222
15.5%
2140
 
9.7%
394
 
6.5%
688
 
6.1%
485
 
5.9%
580
 
5.6%
874
 
5.2%
766
 
4.6%
965
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number974
67.8%
Space Separator462
32.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1222
22.8%
2140
14.4%
394
9.7%
688
 
9.0%
485
 
8.7%
580
 
8.2%
874
 
7.6%
766
 
6.8%
965
 
6.7%
060
 
6.2%
Space Separator
ValueCountFrequency (%)
462
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1436
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
462
32.2%
1222
15.5%
2140
 
9.7%
394
 
6.5%
688
 
6.1%
485
 
5.9%
580
 
5.6%
874
 
5.2%
766
 
4.6%
965
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1436
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
462
32.2%
1222
15.5%
2140
 
9.7%
394
 
6.5%
688
 
6.1%
485
 
5.9%
580
 
5.6%
874
 
5.2%
766
 
4.6%
965
 
4.5%

miejsce_w_polsce
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct459
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean230.416122
Minimum1
Maximum460
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2022-05-10T14:16:43.232849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile23.9
Q1115.5
median230
Q3345.5
95-th percentile437.1
Maximum460
Range459
Interquartile range (IQR)230

Descriptive statistics

Standard deviation133.0676809
Coefficient of variation (CV)0.5775102874
Kurtosis-1.20330389
Mean230.416122
Median Absolute Deviation (MAD)115
Skewness0.001846186286
Sum105761
Variance17707.00769
MonotonicityNot monotonic
2022-05-10T14:16:43.342216image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
0.2%
3171
 
0.2%
1831
 
0.2%
3151
 
0.2%
3121
 
0.2%
3131
 
0.2%
3141
 
0.2%
3111
 
0.2%
3101
 
0.2%
3081
 
0.2%
Other values (449)449
97.8%
ValueCountFrequency (%)
11
0.2%
21
0.2%
31
0.2%
41
0.2%
51
0.2%
61
0.2%
71
0.2%
81
0.2%
91
0.2%
101
0.2%
ValueCountFrequency (%)
4601
0.2%
4591
0.2%
4581
0.2%
4571
0.2%
4561
0.2%
4551
0.2%
4541
0.2%
4531
0.2%
4521
0.2%
4511
0.2%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
1
310 
0
149 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters459
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1310
67.5%
0149
32.5%

Length

2022-05-10T14:16:43.451590image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T14:16:43.535714image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1310
67.5%
0149
32.5%

Most occurring characters

ValueCountFrequency (%)
1310
67.5%
0149
32.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number459
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1310
67.5%
0149
32.5%

Most occurring scripts

ValueCountFrequency (%)
Common459
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1310
67.5%
0149
32.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII459
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1310
67.5%
0149
32.5%

bezpieczeństwo_dezynfekowane_pokoje
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
0
235 
1
224 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters459
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0235
51.2%
1224
48.8%

Length

2022-05-10T14:16:43.601397image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T14:16:43.679511image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0235
51.2%
1224
48.8%

Most occurring characters

ValueCountFrequency (%)
0235
51.2%
1224
48.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number459
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0235
51.2%
1224
48.8%

Most occurring scripts

ValueCountFrequency (%)
Common459
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0235
51.2%
1224
48.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII459
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0235
51.2%
1224
48.8%

podstawowe_klimatyzowany
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
0
264 
1
195 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters459
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0264
57.5%
1195
42.5%

Length

2022-05-10T14:16:43.757632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T14:16:43.835958image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0264
57.5%
1195
42.5%

Most occurring characters

ValueCountFrequency (%)
0264
57.5%
1195
42.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number459
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0264
57.5%
1195
42.5%

Most occurring scripts

ValueCountFrequency (%)
Common459
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0264
57.5%
1195
42.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII459
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0264
57.5%
1195
42.5%

podstawowe_nie_dla_epileptyków
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
0
363 
1
96 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters459
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0363
79.1%
196
 
20.9%

Length

2022-05-10T14:16:43.913866image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T14:16:43.992026image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0363
79.1%
196
 
20.9%

Most occurring characters

ValueCountFrequency (%)
0363
79.1%
196
 
20.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number459
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0363
79.1%
196
 
20.9%

Most occurring scripts

ValueCountFrequency (%)
Common459
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0363
79.1%
196
 
20.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII459
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0363
79.1%
196
 
20.9%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
0
347 
1
112 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters459
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0347
75.6%
1112
 
24.4%

Length

2022-05-10T14:16:44.054526image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T14:16:44.132631image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0347
75.6%
1112
 
24.4%

Most occurring characters

ValueCountFrequency (%)
0347
75.6%
1112
 
24.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number459
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0347
75.6%
1112
 
24.4%

Most occurring scripts

ValueCountFrequency (%)
Common459
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0347
75.6%
1112
 
24.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII459
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0347
75.6%
1112
 
24.4%

bezpieczeństwo_oświetlenie_awaryjne
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
1
234 
0
225 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters459
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1234
51.0%
0225
49.0%

Length

2022-05-10T14:16:44.210758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T14:16:44.289232image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1234
51.0%
0225
49.0%

Most occurring characters

ValueCountFrequency (%)
1234
51.0%
0225
49.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number459
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1234
51.0%
0225
49.0%

Most occurring scripts

ValueCountFrequency (%)
Common459
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1234
51.0%
0225
49.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII459
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1234
51.0%
0225
49.0%

podstawowe_przyjazny_zwierzętom
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
0
385 
1
74 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters459
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0385
83.9%
174
 
16.1%

Length

2022-05-10T14:16:44.351705image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T14:16:44.438644image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0385
83.9%
174
 
16.1%

Most occurring characters

ValueCountFrequency (%)
0385
83.9%
174
 
16.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number459
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0385
83.9%
174
 
16.1%

Most occurring scripts

ValueCountFrequency (%)
Common459
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0385
83.9%
174
 
16.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII459
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0385
83.9%
174
 
16.1%

bezpieczeństwo_dostępny_żel_antybakteryjny
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
1
241 
0
218 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters459
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1241
52.5%
0218
47.5%

Length

2022-05-10T14:16:44.501160image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T14:16:44.767055image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1241
52.5%
0218
47.5%

Most occurring characters

ValueCountFrequency (%)
1241
52.5%
0218
47.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number459
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1241
52.5%
0218
47.5%

Most occurring scripts

ValueCountFrequency (%)
Common459
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1241
52.5%
0218
47.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII459
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1241
52.5%
0218
47.5%

bezpieczeństwo_przycisk_bezpieczeństwa
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
1
241 
0
218 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters459
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1241
52.5%
0218
47.5%

Length

2022-05-10T14:16:44.829273image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T14:16:44.917017image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1241
52.5%
0218
47.5%

Most occurring characters

ValueCountFrequency (%)
1241
52.5%
0218
47.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number459
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1241
52.5%
0218
47.5%

Most occurring scripts

ValueCountFrequency (%)
Common459
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1241
52.5%
0218
47.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII459
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1241
52.5%
0218
47.5%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
0
357 
1
102 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters459
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0357
77.8%
1102
 
22.2%

Length

2022-05-10T14:16:44.979497image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T14:16:45.057616image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0357
77.8%
1102
 
22.2%

Most occurring characters

ValueCountFrequency (%)
0357
77.8%
1102
 
22.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number459
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0357
77.8%
1102
 
22.2%

Most occurring scripts

ValueCountFrequency (%)
Common459
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0357
77.8%
1102
 
22.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII459
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0357
77.8%
1102
 
22.2%

bezpieczeństwo_drzwi_zawsze_otwarte
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
0
249 
1
210 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters459
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0249
54.2%
1210
45.8%

Length

2022-05-10T14:16:45.135770image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T14:16:45.213861image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0249
54.2%
1210
45.8%

Most occurring characters

ValueCountFrequency (%)
0249
54.2%
1210
45.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number459
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0249
54.2%
1210
45.8%

Most occurring scripts

ValueCountFrequency (%)
Common459
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0249
54.2%
1210
45.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII459
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0249
54.2%
1210
45.8%

bezpieczeństwo_pełen_monitoring
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
1
406 
0
53 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters459
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1406
88.5%
053
 
11.5%

Length

2022-05-10T14:16:45.288100image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T14:16:45.366008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1406
88.5%
053
 
11.5%

Most occurring characters

ValueCountFrequency (%)
1406
88.5%
053
 
11.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number459
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1406
88.5%
053
 
11.5%

Most occurring scripts

ValueCountFrequency (%)
Common459
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1406
88.5%
053
 
11.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII459
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1406
88.5%
053
 
11.5%

bezpieczeństwo_pełen_kontakt_z_mistrzem_gry
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
1
408 
0
51 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters459
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1408
88.9%
051
 
11.1%

Length

2022-05-10T14:16:45.428506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T14:16:45.506664image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1408
88.9%
051
 
11.1%

Most occurring characters

ValueCountFrequency (%)
1408
88.9%
051
 
11.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number459
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1408
88.9%
051
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Common459
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1408
88.9%
051
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII459
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1408
88.9%
051
 
11.1%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
0
333 
1
126 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters459
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0333
72.5%
1126
 
27.5%

Length

2022-05-10T14:16:45.584767image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T14:16:45.662876image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0333
72.5%
1126
 
27.5%

Most occurring characters

ValueCountFrequency (%)
0333
72.5%
1126
 
27.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number459
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0333
72.5%
1126
 
27.5%

Most occurring scripts

ValueCountFrequency (%)
Common459
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0333
72.5%
1126
 
27.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII459
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0333
72.5%
1126
 
27.5%

bezpieczeństwo_system_awaryjnego_otwierania_drzwi
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
1
247 
0
212 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters459
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1247
53.8%
0212
46.2%

Length

2022-05-10T14:16:45.734338image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T14:16:45.812459image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1247
53.8%
0212
46.2%

Most occurring characters

ValueCountFrequency (%)
1247
53.8%
0212
46.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number459
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1247
53.8%
0212
46.2%

Most occurring scripts

ValueCountFrequency (%)
Common459
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1247
53.8%
0212
46.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII459
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1247
53.8%
0212
46.2%

języki_angielski
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
1
261 
0
198 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters459
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1261
56.9%
0198
43.1%

Length

2022-05-10T14:16:45.913741image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T14:16:45.991865image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1261
56.9%
0198
43.1%

Most occurring characters

ValueCountFrequency (%)
1261
56.9%
0198
43.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number459
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1261
56.9%
0198
43.1%

Most occurring scripts

ValueCountFrequency (%)
Common459
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1261
56.9%
0198
43.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII459
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1261
56.9%
0198
43.1%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
1
245 
0
214 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters459
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1245
53.4%
0214
46.6%

Length

2022-05-10T14:16:46.088933image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T14:16:46.167056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1245
53.4%
0214
46.6%

Most occurring characters

ValueCountFrequency (%)
1245
53.4%
0214
46.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number459
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1245
53.4%
0214
46.6%

Most occurring scripts

ValueCountFrequency (%)
Common459
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1245
53.4%
0214
46.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII459
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1245
53.4%
0214
46.6%

bezpieczeństwo_dostępne_rekawiczki
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
0
325 
1
134 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters459
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0325
70.8%
1134
29.2%

Length

2022-05-10T14:16:46.245184image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T14:16:46.323327image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0325
70.8%
1134
29.2%

Most occurring characters

ValueCountFrequency (%)
0325
70.8%
1134
29.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number459
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0325
70.8%
1134
29.2%

Most occurring scripts

ValueCountFrequency (%)
Common459
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0325
70.8%
1134
29.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII459
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0325
70.8%
1134
29.2%

podstawowe_nie_dla_kobiet_w_ciąży
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
0
362 
1
97 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters459
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0362
78.9%
197
 
21.1%

Length

2022-05-10T14:16:46.385827image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T14:16:46.463936image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0362
78.9%
197
 
21.1%

Most occurring characters

ValueCountFrequency (%)
0362
78.9%
197
 
21.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number459
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0362
78.9%
197
 
21.1%

Most occurring scripts

ValueCountFrequency (%)
Common459
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0362
78.9%
197
 
21.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII459
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0362
78.9%
197
 
21.1%

języki_polski
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
1
458 
0
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters459
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1458
99.8%
01
 
0.2%

Length

2022-05-10T14:16:46.542055image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T14:16:46.645595image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1458
99.8%
01
 
0.2%

Most occurring characters

ValueCountFrequency (%)
1458
99.8%
01
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number459
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1458
99.8%
01
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common459
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1458
99.8%
01
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII459
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1458
99.8%
01
 
0.2%

podstawowe_przyjazny_dzieciom
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
0
243 
1
216 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters459
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0243
52.9%
1216
47.1%

Length

2022-05-10T14:16:46.741725image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T14:16:46.819855image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0243
52.9%
1216
47.1%

Most occurring characters

ValueCountFrequency (%)
0243
52.9%
1216
47.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number459
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0243
52.9%
1216
47.1%

Most occurring scripts

ValueCountFrequency (%)
Common459
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0243
52.9%
1216
47.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII459
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0243
52.9%
1216
47.1%

podstawowe_od_16_lat
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
0
360 
1
99 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters459
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0360
78.4%
199
 
21.6%

Length

2022-05-10T14:16:46.897979image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T14:16:46.984968image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0360
78.4%
199
 
21.6%

Most occurring characters

ValueCountFrequency (%)
0360
78.4%
199
 
21.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number459
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0360
78.4%
199
 
21.6%

Most occurring scripts

ValueCountFrequency (%)
Common459
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0360
78.4%
199
 
21.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII459
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0360
78.4%
199
 
21.6%

srednia_ocena
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct50
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.576906318
Minimum3.2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2022-05-10T14:16:47.078731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum3.2
5-th percentile6.3
Q18.1
median8.9
Q39.4
95-th percentile9.9
Maximum10
Range6.8
Interquartile range (IQR)1.3

Descriptive statistics

Standard deviation1.119376003
Coefficient of variation (CV)0.1305104616
Kurtosis2.966987406
Mean8.576906318
Median Absolute Deviation (MAD)0.6
Skewness-1.4611482
Sum3936.8
Variance1.253002635
MonotonicityNot monotonic
2022-05-10T14:16:47.203748image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
935
 
7.6%
8.926
 
5.7%
9.724
 
5.2%
9.520
 
4.4%
9.120
 
4.4%
8.819
 
4.1%
9.618
 
3.9%
9.218
 
3.9%
9.818
 
3.9%
8.517
 
3.7%
Other values (40)244
53.2%
ValueCountFrequency (%)
3.22
0.4%
3.61
 
0.2%
4.91
 
0.2%
5.31
 
0.2%
5.41
 
0.2%
5.52
0.4%
5.62
0.4%
5.73
0.7%
5.82
0.4%
63
0.7%
ValueCountFrequency (%)
1016
3.5%
9.98
 
1.7%
9.818
3.9%
9.724
5.2%
9.618
3.9%
9.520
4.4%
9.417
3.7%
9.312
2.6%
9.218
3.9%
9.120
4.4%

ocena_obsluga
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct40
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.422657952
Minimum2.3
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2022-05-10T14:16:47.328737image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.3
5-th percentile7.6
Q19.4
median9.8
Q39.9
95-th percentile10
Maximum10
Range7.7
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.9621486568
Coefficient of variation (CV)0.1021101118
Kurtosis15.15729438
Mean9.422657952
Median Absolute Deviation (MAD)0.2
Skewness-3.489734321
Sum4325
Variance0.9257300378
MonotonicityNot monotonic
2022-05-10T14:16:47.469374image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
10100
21.8%
9.966
14.4%
9.864
13.9%
9.751
11.1%
9.627
 
5.9%
9.523
 
5.0%
9.418
 
3.9%
9.212
 
2.6%
911
 
2.4%
9.310
 
2.2%
Other values (30)77
16.8%
ValueCountFrequency (%)
2.31
 
0.2%
41
 
0.2%
4.11
 
0.2%
4.51
 
0.2%
4.91
 
0.2%
5.21
 
0.2%
5.51
 
0.2%
5.71
 
0.2%
5.91
 
0.2%
63
0.7%
ValueCountFrequency (%)
10100
21.8%
9.966
14.4%
9.864
13.9%
9.751
11.1%
9.627
 
5.9%
9.523
 
5.0%
9.418
 
3.9%
9.310
 
2.2%
9.212
 
2.6%
9.18
 
1.7%

ocena_klimat
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct47
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.036601307
Minimum2.6
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2022-05-10T14:16:47.612708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.6
5-th percentile7
Q18.7
median9.4
Q39.8
95-th percentile10
Maximum10
Range7.4
Interquartile range (IQR)1.1

Descriptive statistics

Standard deviation1.036311373
Coefficient of variation (CV)0.1146793289
Kurtosis6.254704314
Mean9.036601307
Median Absolute Deviation (MAD)0.4
Skewness-2.068358986
Sum4147.8
Variance1.073941262
MonotonicityNot monotonic
2022-05-10T14:16:47.728344image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
1050
 
10.9%
9.836
 
7.8%
9.734
 
7.4%
9.932
 
7.0%
9.629
 
6.3%
9.226
 
5.7%
9.426
 
5.7%
9.523
 
5.0%
9.323
 
5.0%
921
 
4.6%
Other values (37)159
34.6%
ValueCountFrequency (%)
2.61
0.2%
3.31
0.2%
4.91
0.2%
5.11
0.2%
5.31
0.2%
5.51
0.2%
5.81
0.2%
5.91
0.2%
61
0.2%
6.21
0.2%
ValueCountFrequency (%)
1050
10.9%
9.932
7.0%
9.836
7.8%
9.734
7.4%
9.629
6.3%
9.523
5.0%
9.426
5.7%
9.323
5.0%
9.226
5.7%
9.114
 
3.1%
Distinct5
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
Średni
215 
Trudny
206 
Łatwy
28 
Bardzo trudny
 
9
Bardzo łatwy
 
1

Length

Max length13
Median length6
Mean length6.089324619
Min length5

Characters and Unicode

Total characters2795
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st rowŚredni
2nd rowTrudny
3rd rowTrudny
4th rowŚredni
5th rowŚredni

Common Values

ValueCountFrequency (%)
Średni215
46.8%
Trudny206
44.9%
Łatwy28
 
6.1%
Bardzo trudny9
 
2.0%
Bardzo łatwy1
 
0.2%

Length

2022-05-10T14:16:47.837722image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T14:16:47.931471image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
średni215
45.8%
trudny215
45.8%
łatwy29
 
6.2%
bardzo10
 
2.1%

Most occurring characters

ValueCountFrequency (%)
d440
15.7%
r440
15.7%
n430
15.4%
y244
8.7%
Ś215
7.7%
e215
7.7%
i215
7.7%
u215
7.7%
T206
7.4%
a39
 
1.4%
Other values (8)136
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2326
83.2%
Uppercase Letter459
 
16.4%
Space Separator10
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d440
18.9%
r440
18.9%
n430
18.5%
y244
10.5%
e215
9.2%
i215
9.2%
u215
9.2%
a39
 
1.7%
t38
 
1.6%
w29
 
1.2%
Other values (3)21
 
0.9%
Uppercase Letter
ValueCountFrequency (%)
Ś215
46.8%
T206
44.9%
Ł28
 
6.1%
B10
 
2.2%
Space Separator
ValueCountFrequency (%)
10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2785
99.6%
Common10
 
0.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
d440
15.8%
r440
15.8%
n430
15.4%
y244
8.8%
Ś215
7.7%
e215
7.7%
i215
7.7%
u215
7.7%
T206
7.4%
a39
 
1.4%
Other values (7)126
 
4.5%
Common
ValueCountFrequency (%)
10
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2551
91.3%
None244
 
8.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d440
17.2%
r440
17.2%
n430
16.9%
y244
9.6%
e215
8.4%
i215
8.4%
u215
8.4%
T206
8.1%
a39
 
1.5%
t38
 
1.5%
Other values (5)69
 
2.7%
None
ValueCountFrequency (%)
Ś215
88.1%
Ł28
 
11.5%
ł1
 
0.4%
Distinct5
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
2
431 
1
 
15
3
 
11
5
 
1
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters459
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.4%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2431
93.9%
115
 
3.3%
311
 
2.4%
51
 
0.2%
41
 
0.2%

Length

2022-05-10T14:16:48.018818image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-10T14:16:48.096929image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2431
93.9%
115
 
3.3%
311
 
2.4%
51
 
0.2%
41
 
0.2%

Most occurring characters

ValueCountFrequency (%)
2431
93.9%
115
 
3.3%
311
 
2.4%
51
 
0.2%
41
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number459
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2431
93.9%
115
 
3.3%
311
 
2.4%
51
 
0.2%
41
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common459
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2431
93.9%
115
 
3.3%
311
 
2.4%
51
 
0.2%
41
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII459
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2431
93.9%
115
 
3.3%
311
 
2.4%
51
 
0.2%
41
 
0.2%

max_liczba_graczy
Real number (ℝ≥0)

Distinct10
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.278867102
Minimum2
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2022-05-10T14:16:48.175080image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q15
median5
Q36
95-th percentile7.1
Maximum15
Range13
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.233011199
Coefficient of variation (CV)0.2335749651
Kurtosis11.69968601
Mean5.278867102
Median Absolute Deviation (MAD)1
Skewness2.102823138
Sum2423
Variance1.520316618
MonotonicityNot monotonic
2022-05-10T14:16:48.268812image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
5216
47.1%
6113
24.6%
484
 
18.3%
816
 
3.5%
715
 
3.3%
25
 
1.1%
105
 
1.1%
33
 
0.7%
151
 
0.2%
121
 
0.2%
ValueCountFrequency (%)
25
 
1.1%
33
 
0.7%
484
 
18.3%
5216
47.1%
6113
24.6%
715
 
3.3%
816
 
3.5%
105
 
1.1%
121
 
0.2%
151
 
0.2%
ValueCountFrequency (%)
151
 
0.2%
121
 
0.2%
105
 
1.1%
816
 
3.5%
715
 
3.3%
6113
24.6%
5216
47.1%
484
 
18.3%
33
 
0.7%
25
 
1.1%

Interactions

2022-05-10T14:16:40.141998image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-10T14:16:36.726481image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-10T14:16:37.428899image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-10T14:16:38.150597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-10T14:16:38.930191image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-10T14:16:39.555481image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-10T14:16:40.237748image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-10T14:16:36.841810image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-10T14:16:37.533135image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-10T14:16:38.238204image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-10T14:16:39.031311image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-10T14:16:39.651486image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-10T14:16:40.333512image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-10T14:16:36.934691image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-10T14:16:37.620449image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-10T14:16:38.344631image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-10T14:16:39.127309image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-10T14:16:39.739486image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-10T14:16:40.435716image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-10T14:16:37.074242image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-10T14:16:37.822229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-10T14:16:38.518958image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-10T14:16:39.231338image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-10T14:16:39.849335image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-10T14:16:40.531702image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-10T14:16:37.195074image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-10T14:16:37.958224image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-10T14:16:38.705846image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-10T14:16:39.329689image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-10T14:16:39.945040image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-10T14:16:40.627700image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-10T14:16:37.313338image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-10T14:16:38.060503image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-10T14:16:38.822975image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-10T14:16:39.451480image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-10T14:16:40.038047image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-05-10T14:16:48.643835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-10T14:16:49.043456image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-10T14:16:49.402844image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-10T14:16:49.777824image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-05-10T14:16:50.184074image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-10T14:16:40.955208image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-10T14:16:41.992733image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

nazwamiastofirmaczas_grykategoriapoziom_trudnosciliczba_ocenmiejsce_w_polscebezpieczeństwo_tylko_niskie_napięcie_w_zasięgu_graczabezpieczeństwo_dezynfekowane_pokojepodstawowe_klimatyzowanypodstawowe_nie_dla_epileptykówbezpieczeństwo_ograniczony_kontakt_z_innymi_grupamibezpieczeństwo_oświetlenie_awaryjnepodstawowe_przyjazny_zwierzętombezpieczeństwo_dostępny_żel_antybakteryjnybezpieczeństwo_przycisk_bezpieczeństwapodstawowe_nieodpowiednie_dla_osób_z_klaustrofobiąbezpieczeństwo_drzwi_zawsze_otwartebezpieczeństwo_pełen_monitoringbezpieczeństwo_pełen_kontakt_z_mistrzem_grybezpieczeństwo_klucz_bezpieczeństwabezpieczeństwo_system_awaryjnego_otwierania_drzwijęzyki_angielskipodstawowe_możliwość_płatności_kartąbezpieczeństwo_dostępne_rekawiczkipodstawowe_nie_dla_kobiet_w_ciążyjęzyki_polskipodstawowe_przyjazny_dzieciompodstawowe_od_16_latsrednia_ocenaocena_obslugaocena_klimattrudnosc_wg_graczymin_liczba_graczymax_liczba_graczy
0Cicha NocGliwiceTickTack75Przygodowyśr. zaawansowani851110111011111101010111010.010.010.0Średni25
1Powstanie WarszawskieWarszawaBlack Cat Escape Room80Historycznyśr. zaawansowani258211111111111110110111109.89.810.0Trudny28
2LokalizacjaGliwiceTickTack75Fabularnydoświadczony2713100101001111101000110110.010.010.0Trudny25
3Opuszczony HotelWrocławExit19.pl101Thrillerśr. zaawansowani57411111101111110111011019.99.910.0Średni25
4RastamobilWrocławEscape Bus / Escapeo90Przygodowyśr. zaawansowani98511101101111110100001009.910.010.0Średni24
5KrasnoludyPoznańEscapelandia75Przygodowyśr. zaawansowani91611010101100110111011109.910.010.0Średni24
6Moriarty sp. z o.o.KatowiceQuest Cage75Fabularnyśr. zaawansowani434701100001000111010001019.89.99.9Trudny25
7Klątwa CzarnobrodegoBydgoszczMr Lock Escape Room60Przygodowyśr. zaawansowani117810100000111111110011109.89.99.9Trudny24
8SuperheroomBydgoszczMr Lock Escape Room70Przygodowyśr. zaawansowani410910010000110110110011009.810.09.9Trudny25
9Lochy Króla ArturaWrocławExit19.pl90Przygodowyśr. zaawansowani1591010100100100110111001109.89.59.9Trudny25

Last rows

nazwamiastofirmaczas_grykategoriapoziom_trudnosciliczba_ocenmiejsce_w_polscebezpieczeństwo_tylko_niskie_napięcie_w_zasięgu_graczabezpieczeństwo_dezynfekowane_pokojepodstawowe_klimatyzowanypodstawowe_nie_dla_epileptykówbezpieczeństwo_ograniczony_kontakt_z_innymi_grupamibezpieczeństwo_oświetlenie_awaryjnepodstawowe_przyjazny_zwierzętombezpieczeństwo_dostępny_żel_antybakteryjnybezpieczeństwo_przycisk_bezpieczeństwapodstawowe_nieodpowiednie_dla_osób_z_klaustrofobiąbezpieczeństwo_drzwi_zawsze_otwartebezpieczeństwo_pełen_monitoringbezpieczeństwo_pełen_kontakt_z_mistrzem_grybezpieczeństwo_klucz_bezpieczeństwabezpieczeństwo_system_awaryjnego_otwierania_drzwijęzyki_angielskipodstawowe_możliwość_płatności_kartąbezpieczeństwo_dostępne_rekawiczkipodstawowe_nie_dla_kobiet_w_ciążyjęzyki_polskipodstawowe_przyjazny_dzieciompodstawowe_od_16_latsrednia_ocenaocena_obslugaocena_klimattrudnosc_wg_graczymin_liczba_graczymax_liczba_graczy
449Nawiedzona piwnicaJanów LubelskiZoom Natury60Thrillerśr. zaawansowani145200000000000000001001108.06.06.0Średni26
450Gabinet KristeraJedlina-ZdrójZespół Pałacowo-Hotelowy Jedlinka60Fabularnydoświadczony1453000000000000000000010010.010.010.0Trudny24
451Terrorysta. Bomba w Magazynie.SkierniewiceGabinety Tajemnic60Kryminalnyśr. zaawansowani145411001101011110001101008.010.07.0Średni26
452HisteriaSkierniewiceGabinety Tajemnic60Thrillerśr. zaawansowani945611001101001110001101015.610.08.2Bardzo trudny25
453The MistressGdańskEscaperooms.pl60Fabularnyśr. zaawansowani245501100101100110111001007.28.89.6Łatwy24
454Pokój żużlowyGorzów WielkopolskiOpen The Lock60Fabularnydoświadczony145701000101100110100001107.04.07.0Średni24
455Kamienie nieskończonościTarnówTrzy Klucze60Fabularnyśr. zaawansowani445800000000001100001001005.89.56.3Średni25
456SaloonOpoleEscape Room Chata Zagadek60Przygodowyśr. zaawansowani145901101101000111010001106.010.09.0Średni25
457AlicjaKrakówExitRoom ™60Fabularnyśr. zaawansowani146000110000001110011001107.09.08.0Łatwy25
458Łóżko szpitalneKatowiceMystery Machinery45Fabularnydoświadczony143410000101111110001001019.010.09.0Trudny24